We present a framework that allows to certify the fairness degree of a model
based on an interactive and privacy-preserving test. The framework verifies any
trained model, regardless of its training process and architecture. Thus, it
allows us to evaluate any deep learning model on multiple fairness definitions
empirically. We tackle two scenarios, where either the test data is privately
available only to the tester or is publicly known in advance, even to the model
creator. We investigate the soundness of the proposed approach using
theoretical analysis and present statistical guarantees for the interactive
test. Finally, we provide a cryptographic technique to automate fairness
testing and certified inference with only black-box access to the model at hand
while hiding the participants' sensitive data.

本文介绍了一个基于交互和隐私保护测试的框架，该框架允许对任何经过训练的模型进行公平程度的认证，无论其训练过程和架构如何，并且提供了一种加密技术来自动进行公平测试和认证推理，同时隐藏参与者的敏感数据。